Optimization of an absorption heat transformer with two-duplex components using inverse neural network and solved by genetic algorithm

Morales, L.I.; Conde-Gutiérrez, R.A.; Hernández, J.A.; Huicochea, A.; Juárez-Romero, D.; Siqueiros, J.

doi:10.1016/j.applthermaleng.2015.04.018

Cited by 24 publications

(3 citation statements)

References 30 publications

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“…According to Hernández (2009); Labus et al (2012); Hernández et al (2012); Laidi and Hanin, (2013); Hernández (2013); Morales et al (2015); the artificial neural network can be inverted to calculated a desired input parameter. In order to apply this inverse artificial neural network (ANNi), first it is necessary to have the ANN model.…”

Section: Inverse Artificial Neural Networkmentioning

confidence: 99%

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Estimación de desempeño y optimización de un sistema de absorción adiabático H2OLiBr usando redes neuronales artificiales

Gutiérrez-Urueta

Colorado

Hernández

et al. 2019

iit

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The search for alternatives to curb climate change and its devastating consequences for today's society, leads to research environmentally friendly climate systems. To optimize or control them, artificial neural networks (ANN) is considered an effective option. Adiabatic absorption is based on separate design for heat and mass transfer process in order to reduce the size of equipment. This study deals with the application of ANN on the experimental results of a single effect water-lithium bromide adiabatic absorption facility and its optimization using an inverse ANN. Transient and steady state data were used to obtain three empirical models. The models developed correspond to the coefficient of performance (COP), cooling power and generation power of the facility. Steady state statistics consists of 219 experimental points obtained at different operating conditions. These data were used to train and test the steady state and transient ANN models. For transient statistics, 1445 values were considered for a period. In the validation data set, the results showed that simulations and the experimental data were in good agreement with an R > 0.98 for both steady state and transient models. A model for COP, based on the principle of accessibility of data, was developed including temperatures for the external fluid circuits with good results. The inverse neural model applied to transient data demonstrated satisfactory results as well, making possible the optimization of the facility. These results illustrate the adequacy in using an ANN with transient data in absorption systems, making it especially attractive for solar cooling applications.

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Section: Inverse Artificial Neural Networkmentioning

confidence: 99%

“…The use of inverse ANN has been successfully applied to predict the optimal operation conditions for a single-stage heat transformer (Colorado et al, 2011), the optimum coefficient of performance of a heat trans-former (Morales et al, 2015) and polygeneration systems (Hernández et al, 2013) among others.…”

Section: Introductionmentioning

confidence: 99%

Estimación de desempeño y optimización de un sistema de absorción adiabático H2OLiBr usando redes neuronales artificiales

Gutiérrez-Urueta

Colorado

Hernández

et al. 2019

iit

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“…By pairing ANN with the evolutionary algorithms, the inverse approach can effectively be used for the estimation of unknown parameters with less computational time. (Morales et al, 2015;Vakili et al, 2017;Chanda et al, 2017;Famouri et al, 2013, Yadav et al, 2019. The neural network obtained by training the inputs and corresponding simulated temperatures is found to be capable of estimating unknowns for measured temperature data (Jakkareddy and Balaji, 2018).…”

Section: Introductionmentioning

confidence: 99%

3D coupled conduction-convection problem using in-house heat transfer experiments in conjunction with hybrid inverse approach

Vishweshwara

Kumar

Gnanasekaran

et al. 2019

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Purpose Many a times, the information about the boundary heat flux is obtained only through inverse approach by locating the thermocouple or temperature sensor in accessible boundary. Most of the work reported in literature for the estimation of unknown parameters is based on heat conduction model. Inverse approach using conjugate heat transfer is found inadequate in literature. Therefore, the purpose of the paper is to develop a 3D conjugate heat transfer model without model reduction for the estimation of heat flux and heat transfer coefficient from the measured temperatures. Design/methodology/approach A 3 D conjugate fin heat transfer model is solved using commercial software for the known boundary conditions. Navier–Stokes equation is solved to obtain the necessary temperature distribution of the fin. Later, the complete model is replaced with neural network to expedite the computations of the forward problem. For the inverse approach, genetic algorithm (GA) and particle swarm optimization (PSO) are applied to estimate the unknown parameters. Eventually, a hybrid algorithm is proposed by combining PSO with Broyden–Fletcher–Goldfarb–Shanno (BFGS) method that outperforms GA and PSO. Findings The authors demonstrate that the evolutionary algorithms can be used to obtain accurate results from simulated measurements. Efficacy of the hybrid algorithm is established using real time measurements. The hybrid algorithm (PSO-BFGS) is more efficient in the estimation of unknown parameters for experimentally measured temperature data compared to GA and PSO algorithms. Originality/value Surrogate model using ANN based on computational fluid dynamics simulations and in-house steady state fin experiments to estimate the heat flux and heat transfer coefficient separately using GA, PSO and PSO-BFGS.

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Analysis of asynchronous distributed multi-master parallel genetic algorithm optimization on CAN bus

2020

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Optimization of an absorption heat transformer with two-duplex components using inverse neural network and solved by genetic algorithm

Cited by 24 publications

References 30 publications

Estimación de desempeño y optimización de un sistema de absorción adiabático H2OLiBr usando redes neuronales artificiales

Estimación de desempeño y optimización de un sistema de absorción adiabático H2OLiBr usando redes neuronales artificiales

3D coupled conduction-convection problem using in-house heat transfer experiments in conjunction with hybrid inverse approach

Analysis of asynchronous distributed multi-master parallel genetic algorithm optimization on CAN bus

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